Abstract
In Positron Emission Tomography (PET), an optimal estimate of the radioactivity concentration is obtained from the measured emission data under certain criteria. So far, all the well-known statistical reconstruction algorithms require exactly known system probability matrix a priori, and the quality of such system model largely determines the quality of the reconstructed images. In this paper, we propose an algorithm for PET image reconstruction for the real world case where the PET system model is subject to uncertainties. The method counts PET reconstruction as a regularization problem and the image estimation is achieved by means of an uncertainty weighted least squares framework. The performance of our work is evaluated with the Shepp-Logan simulated and real phantom data, which demonstrates significant improvements in image quality over the least squares reconstruction efforts.
Highlights
Positron Emission Tomography (PET) is one of the most important medical imaging modality which provides in vivo functional information of biological organs
We investigate the application of the uncertainty weighted least squares principle to PET image reconstruction
UPWLS Solution for PET Image Reconstruction To solve the objective function (15), we model uncertainties in system and measurement with a norm-bounded structure [23] as 1⁄2dD,dy~HD1⁄2Ed,Ey
Summary
Positron Emission Tomography (PET) is one of the most important medical imaging modality which provides in vivo functional information of biological organs. It utilizes the idea of injecting chemical compounds tagged with positron emitting isotopes into a body to acquire complete coincidence data, which records the concentration information of the isotope distributions at specific locations within the body. Because of the random nature of the radioactive disintegration, the tomographic data are noisy, and it is straight-forward to regard PET reconstruction as a statistical estimation problem. Poisson/Gaussian assumptions on photon counting measurement data may be employed to deal with measurement uncertainties, constraining the solution space of reconstruction problem in maximum likelihood/least squares based frameworks [7,8,9,10,11]
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